{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "77e59e0d-508b-4e51-992b-d97e35797c11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w= [2.13756953] b= [90.78228498]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "x=np.array([[100],[113],[90],[89],[60],[70],[50],[45],[55],[78]])\n",
    "y=np.array([[301],[324],[285],[296],[200],[260],[300],[120],[180],[245]])\n",
    "model=LinearRegression()\n",
    "model.fit(x,y)\n",
    "print(\"w=\",model.coef_[0],\"b=\",model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5016335c-2001-4727-acff-e9c9e9762762",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot  as plt \n",
    "y2=model.predict(x)\n",
    "plt.xlabel('面积')\n",
    "plt.ylabel('售价')\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.axis([40,125,100,400])\n",
    "plt.scatter (x,y,s=60,c='k',marker='o')\n",
    "plt.plot(x,y,'r-')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9cb974e-6191-4b94-a311-05c2dfe40642",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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